Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations132155
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.3 MiB
Average record size in memory795.8 B

Variable types

DateTime2
Text7
Numeric16
Boolean1
Categorical2

Alerts

Energy is highly overall correlated with LoudnessHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Track Duration (min) is highly overall correlated with Track Duration (ms)High correlation
Track Duration (ms) is highly overall correlated with Track Duration (min)High correlation
last_weeks_rank is highly overall correlated with weeks_on_chart and 1 other fieldsHigh correlation
weeks_on_chart is highly overall correlated with last_weeks_rankHigh correlation
weeks_rank is highly overall correlated with last_weeks_rankHigh correlation
Time Signature is highly imbalanced (82.1%) Imbalance
Key has 13763 (10.4%) zeros Zeros
Instrumentalness has 79224 (59.9%) zeros Zeros

Reproduction

Analysis started2025-06-23 16:36:18.191774
Analysis finished2025-06-23 16:37:06.394833
Duration48.2 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

date
Date

Distinct1324
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2000-01-02 00:00:00
Maximum2025-05-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-23T12:37:06.596296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:06.795303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct10031
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
2025-06-23T12:37:07.230066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length55
Mean length13.13578
Min length1

Characters and Unicode

Total characters1735959
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2313 ?
Unique (%)1.8%

Sample

1st rowSmooth
2nd rowBack At One
3rd rowI Wanna Love You Forever
4th rowMy Love Is Your Love
5th rowHot Boyz
ValueCountFrequency (%)
you 11322
 
3.2%
the 10138
 
2.9%
me 8162
 
2.3%
i 7767
 
2.2%
it 5717
 
1.6%
a 5358
 
1.5%
love 5328
 
1.5%
my 4411
 
1.2%
to 4016
 
1.1%
in 3713
 
1.0%
Other values (5845) 288662
81.4%
2025-06-23T12:37:07.755172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
222455
 
12.8%
e 160930
 
9.3%
o 117664
 
6.8%
a 90006
 
5.2%
n 85499
 
4.9%
t 82195
 
4.7%
i 77713
 
4.5%
r 72010
 
4.1%
l 52960
 
3.1%
s 52870
 
3.0%
Other values (75) 721657
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1735959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
222455
 
12.8%
e 160930
 
9.3%
o 117664
 
6.8%
a 90006
 
5.2%
n 85499
 
4.9%
t 82195
 
4.7%
i 77713
 
4.5%
r 72010
 
4.1%
l 52960
 
3.1%
s 52870
 
3.0%
Other values (75) 721657
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1735959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
222455
 
12.8%
e 160930
 
9.3%
o 117664
 
6.8%
a 90006
 
5.2%
n 85499
 
4.9%
t 82195
 
4.7%
i 77713
 
4.5%
r 72010
 
4.1%
l 52960
 
3.1%
s 52870
 
3.0%
Other values (75) 721657
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1735959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
222455
 
12.8%
e 160930
 
9.3%
o 117664
 
6.8%
a 90006
 
5.2%
n 85499
 
4.9%
t 82195
 
4.7%
i 77713
 
4.5%
r 72010
 
4.1%
l 52960
 
3.1%
s 52870
 
3.0%
Other values (75) 721657
41.6%

artist
Text

Distinct5024
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2025-06-23T12:37:08.058856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length113
Median length84
Mean length17.05352
Min length1

Characters and Unicode

Total characters2253708
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique941 ?
Unique (%)0.7%

Sample

1st rowSantana Featuring Rob Thomas
2nd rowBrian McKnight
3rd rowJessica Simpson
4th rowWhitney Houston
5th rowMissy "Misdemeanor" Elliott Featuring NAS, EVE & Q-Tip
ValueCountFrequency (%)
featuring 29133
 
7.7%
15933
 
4.2%
the 6104
 
1.6%
lil 5959
 
1.6%
drake 3370
 
0.9%
brown 2727
 
0.7%
chris 2666
 
0.7%
wayne 2042
 
0.5%
justin 2021
 
0.5%
young 1948
 
0.5%
Other values (3756) 308361
81.1%
2025-06-23T12:37:08.529599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
248115
 
11.0%
e 192331
 
8.5%
a 189163
 
8.4%
i 149942
 
6.7%
n 145870
 
6.5%
r 130600
 
5.8%
o 95514
 
4.2%
l 88224
 
3.9%
t 88078
 
3.9%
u 68183
 
3.0%
Other values (68) 857688
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2253708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
248115
 
11.0%
e 192331
 
8.5%
a 189163
 
8.4%
i 149942
 
6.7%
n 145870
 
6.5%
r 130600
 
5.8%
o 95514
 
4.2%
l 88224
 
3.9%
t 88078
 
3.9%
u 68183
 
3.0%
Other values (68) 857688
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2253708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
248115
 
11.0%
e 192331
 
8.5%
a 189163
 
8.4%
i 149942
 
6.7%
n 145870
 
6.5%
r 130600
 
5.8%
o 95514
 
4.2%
l 88224
 
3.9%
t 88078
 
3.9%
u 68183
 
3.0%
Other values (68) 857688
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2253708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
248115
 
11.0%
e 192331
 
8.5%
a 189163
 
8.4%
i 149942
 
6.7%
n 145870
 
6.5%
r 130600
 
5.8%
o 95514
 
4.2%
l 88224
 
3.9%
t 88078
 
3.9%
u 68183
 
3.0%
Other values (68) 857688
38.1%

weeks_rank
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.468374
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:08.650552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.86325
Coefficient of variation (CV)0.57190765
Kurtosis-1.1998931
Mean50.468374
Median Absolute Deviation (MAD)25
Skewness0.001342403
Sum6669648
Variance833.08718
MonotonicityNot monotonic
2025-06-23T12:37:08.786085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1324
 
1.0%
3 1324
 
1.0%
6 1324
 
1.0%
5 1324
 
1.0%
32 1324
 
1.0%
27 1324
 
1.0%
7 1324
 
1.0%
8 1324
 
1.0%
10 1324
 
1.0%
13 1324
 
1.0%
Other values (90) 118915
90.0%
ValueCountFrequency (%)
1 1323
1.0%
2 1324
1.0%
3 1324
1.0%
4 1323
1.0%
5 1324
1.0%
6 1324
1.0%
7 1324
1.0%
8 1324
1.0%
9 1322
1.0%
10 1324
1.0%
ValueCountFrequency (%)
100 1320
1.0%
99 1317
1.0%
98 1319
1.0%
97 1322
1.0%
96 1321
1.0%
95 1319
1.0%
94 1316
1.0%
93 1318
1.0%
92 1317
1.0%
91 1318
1.0%

last_weeks_rank
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.896871
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:08.920969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median52
Q380
95-th percentile101
Maximum101
Range100
Interquartile range (IQR)54

Descriptive statistics

Standard deviation31.134352
Coefficient of variation (CV)0.58858589
Kurtosis-1.2370321
Mean52.896871
Median Absolute Deviation (MAD)27
Skewness0.046686366
Sum6990586
Variance969.34788
MonotonicityNot monotonic
2025-06-23T12:37:09.057657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 13827
 
10.5%
7 1320
 
1.0%
5 1320
 
1.0%
6 1320
 
1.0%
2 1320
 
1.0%
8 1319
 
1.0%
10 1319
 
1.0%
3 1318
 
1.0%
1 1318
 
1.0%
14 1317
 
1.0%
Other values (91) 106457
80.6%
ValueCountFrequency (%)
1 1318
1.0%
2 1320
1.0%
3 1318
1.0%
4 1317
1.0%
5 1320
1.0%
6 1320
1.0%
7 1320
1.0%
8 1319
1.0%
9 1316
1.0%
10 1319
1.0%
ValueCountFrequency (%)
101 13827
10.5%
100 523
 
0.4%
99 630
 
0.5%
98 701
 
0.5%
97 723
 
0.5%
96 765
 
0.6%
95 805
 
0.6%
94 827
 
0.6%
93 896
 
0.7%
92 908
 
0.7%

weeks_on_chart
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.977685
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:09.206118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q317
95-th percentile32
Maximum91
Range90
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.020179
Coefficient of variation (CV)0.83657059
Kurtosis4.2881099
Mean11.977685
Median Absolute Deviation (MAD)6
Skewness1.6561696
Sum1582911
Variance100.40399
MonotonicityNot monotonic
2025-06-23T12:37:09.349715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11176
 
8.5%
2 8340
 
6.3%
3 7417
 
5.6%
4 6918
 
5.2%
5 6570
 
5.0%
6 6285
 
4.8%
7 6054
 
4.6%
8 5845
 
4.4%
9 5644
 
4.3%
10 5438
 
4.1%
Other values (81) 62468
47.3%
ValueCountFrequency (%)
1 11176
8.5%
2 8340
6.3%
3 7417
5.6%
4 6918
5.2%
5 6570
5.0%
6 6285
4.8%
7 6054
4.6%
8 5845
4.4%
9 5644
4.3%
10 5438
4.1%
ValueCountFrequency (%)
91 1
 
< 0.1%
90 3
< 0.1%
89 3
< 0.1%
88 3
< 0.1%
87 4
< 0.1%
86 4
< 0.1%
85 4
< 0.1%
84 4
< 0.1%
83 4
< 0.1%
82 4
< 0.1%
Distinct10382
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-06-23T12:37:09.673651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length141
Median length93
Mean length17.234709
Min length1

Characters and Unicode

Total characters2277653
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2476 ?
Unique (%)1.9%

Sample

1st rowSmooth (feat. Rob Thomas)
2nd rowBack At One
3rd rowI Wanna Love You Forever
4th rowMy Love Is Your Love
5th rowHot Boyz
ValueCountFrequency (%)
feat 16406
 
3.7%
the 11249
 
2.5%
you 11220
 
2.5%
10442
 
2.4%
me 8215
 
1.9%
i 7836
 
1.8%
it 5743
 
1.3%
a 5418
 
1.2%
love 5321
 
1.2%
with 4398
 
1.0%
Other values (6942) 357105
80.5%
2025-06-23T12:37:10.187697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
311198
 
13.7%
e 201410
 
8.8%
o 136439
 
6.0%
a 131907
 
5.8%
t 114890
 
5.0%
i 106081
 
4.7%
n 102617
 
4.5%
r 89436
 
3.9%
l 65662
 
2.9%
s 63579
 
2.8%
Other values (106) 954434
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2277653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
311198
 
13.7%
e 201410
 
8.8%
o 136439
 
6.0%
a 131907
 
5.8%
t 114890
 
5.0%
i 106081
 
4.7%
n 102617
 
4.5%
r 89436
 
3.9%
l 65662
 
2.9%
s 63579
 
2.8%
Other values (106) 954434
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2277653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
311198
 
13.7%
e 201410
 
8.8%
o 136439
 
6.0%
a 131907
 
5.8%
t 114890
 
5.0%
i 106081
 
4.7%
n 102617
 
4.5%
r 89436
 
3.9%
l 65662
 
2.9%
s 63579
 
2.8%
Other values (106) 954434
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2277653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
311198
 
13.7%
e 201410
 
8.8%
o 136439
 
6.0%
a 131907
 
5.8%
t 114890
 
5.0%
i 106081
 
4.7%
n 102617
 
4.5%
r 89436
 
3.9%
l 65662
 
2.9%
s 63579
 
2.8%
Other values (106) 954434
41.9%

Tempo
Real number (ℝ)

Distinct9269
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.51364
Minimum48.718
Maximum215.338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:10.318416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48.718
5-th percentile79.028
Q197.476
median120.024
Q3140.858
95-th percentile173.727
Maximum215.338
Range166.62
Interquartile range (IQR)43.382

Descriptive statistics

Standard deviation29.198043
Coefficient of variation (CV)0.24028613
Kurtosis-0.47424244
Mean121.51364
Median Absolute Deviation (MAD)22.012
Skewness0.38558096
Sum16058636
Variance852.52573
MonotonicityNot monotonic
2025-06-23T12:37:10.468498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.011 218
 
0.2%
129.992 149
 
0.1%
139.995 126
 
0.1%
121.927 116
 
0.1%
120.028 106
 
0.1%
100.002 105
 
0.1%
130.003 104
 
0.1%
97.994 103
 
0.1%
140.022 99
 
0.1%
128.007 98
 
0.1%
Other values (9259) 130931
99.1%
ValueCountFrequency (%)
48.718 6
 
< 0.1%
51.316 20
 
< 0.1%
52.63 1
 
< 0.1%
53.376 1
 
< 0.1%
53.863 17
 
< 0.1%
57.967 3
 
< 0.1%
58.876 1
 
< 0.1%
59.715 52
< 0.1%
59.972 20
 
< 0.1%
59.989 40
< 0.1%
ValueCountFrequency (%)
215.338 2
 
< 0.1%
214.13 16
< 0.1%
213.737 1
 
< 0.1%
210.893 30
< 0.1%
209.966 11
 
< 0.1%
209.942 12
 
< 0.1%
209.767 1
 
< 0.1%
208.138 8
 
< 0.1%
208.078 16
< 0.1%
208.067 20
< 0.1%

uri
Text

Distinct11030
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
2025-06-23T12:37:10.727806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters4757580
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2710 ?
Unique (%)2.1%

Sample

1st rowspotify:track:0n2SEXB2qoRQg171q7XqeW
2nd rowspotify:track:6mwA6YiKDjAUG8kWvRRUPh
3rd rowspotify:track:5gZEhPrN1VLqTG1nIAXeNK
4th rowspotify:track:1ckU1EhAO0Nr73QYw24SWJ
5th rowspotify:track:7mYvtEeBdMqRSyj1Qpv6my
ValueCountFrequency (%)
spotify:track:3usxtqrwsyz57ewm6wwrmp 91
 
0.1%
spotify:track:17phhzdn6ogtzme56nuwvj 90
 
0.1%
spotify:track:0vjijw4gluzamyd2vxmi3b 90
 
0.1%
spotify:track:4g8gkoterjn0ywt6uhqbhp 87
 
0.1%
spotify:track:7wdzle2gsx1rgqbvyzhasz 86
 
0.1%
spotify:track:7dfnq8fyhhmcylykf6zcxa 85
 
0.1%
spotify:track:7uep5u2qkdzbipn2ya6lr0 79
 
0.1%
spotify:track:5nujrmhlynf4ymomtj8aqf 77
 
0.1%
spotify:track:3ee8jmje8o58chk66qrvc2 76
 
0.1%
spotify:track:3s0oxqeoh0w6ay8wqvckrw 76
 
0.1%
Other values (11020) 131318
99.4%
2025-06-23T12:37:11.074331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 308845
 
6.5%
: 264310
 
5.6%
o 179385
 
3.8%
k 178238
 
3.7%
y 177659
 
3.7%
r 177527
 
3.7%
i 177506
 
3.7%
a 176957
 
3.7%
s 175522
 
3.7%
f 175462
 
3.7%
Other values (53) 2766169
58.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4757580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 308845
 
6.5%
: 264310
 
5.6%
o 179385
 
3.8%
k 178238
 
3.7%
y 177659
 
3.7%
r 177527
 
3.7%
i 177506
 
3.7%
a 176957
 
3.7%
s 175522
 
3.7%
f 175462
 
3.7%
Other values (53) 2766169
58.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4757580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 308845
 
6.5%
: 264310
 
5.6%
o 179385
 
3.8%
k 178238
 
3.7%
y 177659
 
3.7%
r 177527
 
3.7%
i 177506
 
3.7%
a 176957
 
3.7%
s 175522
 
3.7%
f 175462
 
3.7%
Other values (53) 2766169
58.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4757580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 308845
 
6.5%
: 264310
 
5.6%
o 179385
 
3.8%
k 178238
 
3.7%
y 177659
 
3.7%
r 177527
 
3.7%
i 177506
 
3.7%
a 176957
 
3.7%
s 175522
 
3.7%
f 175462
 
3.7%
Other values (53) 2766169
58.1%
Distinct4785
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2025-06-23T12:37:11.436155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length310
Median length103
Mean length14.752132
Min length1

Characters and Unicode

Total characters1949568
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique853 ?
Unique (%)0.6%

Sample

1st rowSantana, Rob Thomas
2nd rowBrian McKnight
3rd rowJessica Simpson
4th rowWhitney Houston
5th rowMissy Elliott
ValueCountFrequency (%)
the 5908
 
1.8%
lil 5519
 
1.7%
drake 3320
 
1.0%
brown 2681
 
0.8%
chris 2636
 
0.8%
justin 2027
 
0.6%
wayne 1918
 
0.6%
j 1912
 
0.6%
1842
 
0.6%
taylor 1795
 
0.5%
Other values (3825) 300562
91.0%
2025-06-23T12:37:12.000659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
197965
 
10.2%
a 158778
 
8.1%
e 157743
 
8.1%
i 118245
 
6.1%
n 114580
 
5.9%
r 99793
 
5.1%
o 93648
 
4.8%
l 86626
 
4.4%
s 62815
 
3.2%
t 56604
 
2.9%
Other values (89) 802771
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1949568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
197965
 
10.2%
a 158778
 
8.1%
e 157743
 
8.1%
i 118245
 
6.1%
n 114580
 
5.9%
r 99793
 
5.1%
o 93648
 
4.8%
l 86626
 
4.4%
s 62815
 
3.2%
t 56604
 
2.9%
Other values (89) 802771
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1949568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
197965
 
10.2%
a 158778
 
8.1%
e 157743
 
8.1%
i 118245
 
6.1%
n 114580
 
5.9%
r 99793
 
5.1%
o 93648
 
4.8%
l 86626
 
4.4%
s 62815
 
3.2%
t 56604
 
2.9%
Other values (89) 802771
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1949568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
197965
 
10.2%
a 158778
 
8.1%
e 157743
 
8.1%
i 118245
 
6.1%
n 114580
 
5.9%
r 99793
 
5.1%
o 93648
 
4.8%
l 86626
 
4.4%
s 62815
 
3.2%
t 56604
 
2.9%
Other values (89) 802771
41.2%
Distinct5432
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-06-23T12:37:12.374187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length110
Median length78
Mean length17.396663
Min length1

Characters and Unicode

Total characters2299056
Distinct characters126
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique711 ?
Unique (%)0.5%

Sample

1st rowSupernatural (Remastered)
2nd rowBack At One
3rd rowSweet Kisses
4th rowMy Love Is Your Love
5th rowDa Real World
ValueCountFrequency (%)
the 21330
 
5.2%
deluxe 10397
 
2.6%
edition 7385
 
1.8%
6087
 
1.5%
of 5413
 
1.3%
a 4893
 
1.2%
you 4425
 
1.1%
version 3674
 
0.9%
i 3546
 
0.9%
to 3404
 
0.8%
Other values (5105) 336015
82.6%
2025-06-23T12:37:12.928746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
274394
 
11.9%
e 213747
 
9.3%
o 128357
 
5.6%
i 120892
 
5.3%
a 113375
 
4.9%
n 112151
 
4.9%
t 102841
 
4.5%
r 97877
 
4.3%
l 79235
 
3.4%
s 75864
 
3.3%
Other values (116) 980323
42.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2299056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
274394
 
11.9%
e 213747
 
9.3%
o 128357
 
5.6%
i 120892
 
5.3%
a 113375
 
4.9%
n 112151
 
4.9%
t 102841
 
4.5%
r 97877
 
4.3%
l 79235
 
3.4%
s 75864
 
3.3%
Other values (116) 980323
42.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2299056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
274394
 
11.9%
e 213747
 
9.3%
o 128357
 
5.6%
i 120892
 
5.3%
a 113375
 
4.9%
n 112151
 
4.9%
t 102841
 
4.5%
r 97877
 
4.3%
l 79235
 
3.4%
s 75864
 
3.3%
Other values (116) 980323
42.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2299056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
274394
 
11.9%
e 213747
 
9.3%
o 128357
 
5.6%
i 120892
 
5.3%
a 113375
 
4.9%
n 112151
 
4.9%
t 102841
 
4.5%
r 97877
 
4.3%
l 79235
 
3.4%
s 75864
 
3.3%
Other values (116) 980323
42.6%
Distinct2308
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum1945-01-01 00:00:00
Maximum2025-05-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-23T12:37:13.053351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:13.230742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Track Duration (ms)
Real number (ℝ)

High correlation 

Distinct8765
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222929.77
Minimum34400
Maximum740010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:13.385797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34400
5-th percentile158793.5
Q1196093
median220306
Q3245426
95-th percentile294866
Maximum740010
Range705610
Interquartile range (IQR)49333

Descriptive statistics

Standard deviation43555.669
Coefficient of variation (CV)0.19537843
Kurtosis5.3881906
Mean222929.77
Median Absolute Deviation (MAD)24667
Skewness0.97075195
Sum2.9461284 × 1010
Variance1.8970963 × 109
MonotonicityNot monotonic
2025-06-23T12:37:13.980165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231933 153
 
0.1%
200040 110
 
0.1%
219080 107
 
0.1%
256000 106
 
0.1%
229040 106
 
0.1%
191600 103
 
0.1%
232533 101
 
0.1%
213493 100
 
0.1%
186066 100
 
0.1%
229360 98
 
0.1%
Other values (8755) 131071
99.2%
ValueCountFrequency (%)
34400 3
 
< 0.1%
37013 1
 
< 0.1%
39732 13
< 0.1%
41487 1
 
< 0.1%
47354 1
 
< 0.1%
48000 1
 
< 0.1%
49292 1
 
< 0.1%
52195 1
 
< 0.1%
53442 1
 
< 0.1%
68800 17
< 0.1%
ValueCountFrequency (%)
740010 1
 
< 0.1%
688453 22
< 0.1%
620101 1
 
< 0.1%
613026 1
 
< 0.1%
588139 1
 
< 0.1%
587364 2
 
< 0.1%
547733 13
< 0.1%
538536 2
 
< 0.1%
535962 13
< 0.1%
530253 1
 
< 0.1%

Explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
False
89931 
True
42224 
ValueCountFrequency (%)
False 89931
68.0%
True 42224
32.0%
2025-06-23T12:37:14.090741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Popularity
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.14281
Minimum0
Maximum100
Zeros59
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:14.201570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q157
median68
Q377
95-th percentile86
Maximum100
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.730072
Coefficient of variation (CV)0.22270103
Kurtosis0.88357441
Mean66.14281
Median Absolute Deviation (MAD)10
Skewness-0.79079728
Sum8741103
Variance216.97501
MonotonicityNot monotonic
2025-06-23T12:37:14.357124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 4090
 
3.1%
71 3902
 
3.0%
74 3895
 
2.9%
68 3871
 
2.9%
70 3827
 
2.9%
75 3742
 
2.8%
78 3645
 
2.8%
73 3582
 
2.7%
67 3498
 
2.6%
66 3471
 
2.6%
Other values (90) 94632
71.6%
ValueCountFrequency (%)
0 59
< 0.1%
1 27
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 58
< 0.1%
5 65
< 0.1%
6 64
< 0.1%
7 45
< 0.1%
8 73
0.1%
9 23
 
< 0.1%
ValueCountFrequency (%)
100 51
 
< 0.1%
98 56
 
< 0.1%
97 29
 
< 0.1%
96 81
 
0.1%
95 171
 
0.1%
94 211
 
0.2%
93 174
 
0.1%
92 171
 
0.1%
91 477
0.4%
90 949
0.7%

Danceability
Real number (ℝ)

Distinct770
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64516293
Minimum0.0939
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:14.499077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0939
5-th percentile0.407
Q10.548
median0.649
Q30.747
95-th percentile0.88
Maximum0.986
Range0.8921
Interquartile range (IQR)0.199

Descriptive statistics

Standard deviation0.14357716
Coefficient of variation (CV)0.22254403
Kurtosis-0.28242813
Mean0.64516293
Median Absolute Deviation (MAD)0.1
Skewness-0.17884172
Sum85261.507
Variance0.020614401
MonotonicityNot monotonic
2025-06-23T12:37:14.634377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.655 546
 
0.4%
0.647 507
 
0.4%
0.697 502
 
0.4%
0.561 500
 
0.4%
0.637 497
 
0.4%
0.652 494
 
0.4%
0.616 482
 
0.4%
0.688 474
 
0.4%
0.674 472
 
0.4%
0.662 472
 
0.4%
Other values (760) 127209
96.3%
ValueCountFrequency (%)
0.0939 3
 
< 0.1%
0.109 1
 
< 0.1%
0.113 5
 
< 0.1%
0.13 1
 
< 0.1%
0.137 1
 
< 0.1%
0.142 16
< 0.1%
0.144 1
 
< 0.1%
0.148 11
< 0.1%
0.155 1
 
< 0.1%
0.16 5
 
< 0.1%
ValueCountFrequency (%)
0.986 9
 
< 0.1%
0.981 3
 
< 0.1%
0.98 1
 
< 0.1%
0.978 3
 
< 0.1%
0.975 26
< 0.1%
0.974 34
< 0.1%
0.972 1
 
< 0.1%
0.971 12
 
< 0.1%
0.968 10
 
< 0.1%
0.967 36
< 0.1%

Energy
Real number (ℝ)

High correlation 

Distinct840
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68084689
Minimum0.00174
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:14.795954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00174
5-th percentile0.395
Q10.571
median0.698
Q30.807
95-th percentile0.92
Maximum0.996
Range0.99426
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.16344866
Coefficient of variation (CV)0.24006668
Kurtosis-0.14024545
Mean0.68084689
Median Absolute Deviation (MAD)0.116
Skewness-0.50095576
Sum89977.321
Variance0.026715463
MonotonicityNot monotonic
2025-06-23T12:37:14.978648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.825 501
 
0.4%
0.73 500
 
0.4%
0.733 492
 
0.4%
0.678 478
 
0.4%
0.783 462
 
0.3%
0.715 461
 
0.3%
0.648 460
 
0.3%
0.621 458
 
0.3%
0.691 443
 
0.3%
0.538 432
 
0.3%
Other values (830) 127468
96.5%
ValueCountFrequency (%)
0.00174 3
 
< 0.1%
0.00332 18
< 0.1%
0.0302 5
 
< 0.1%
0.0316 1
 
< 0.1%
0.0561 1
 
< 0.1%
0.0625 1
 
< 0.1%
0.0645 2
 
< 0.1%
0.0668 1
 
< 0.1%
0.068 2
 
< 0.1%
0.07 4
 
< 0.1%
ValueCountFrequency (%)
0.996 10
 
< 0.1%
0.993 21
< 0.1%
0.991 12
 
< 0.1%
0.99 20
< 0.1%
0.989 49
< 0.1%
0.988 40
< 0.1%
0.987 4
 
< 0.1%
0.986 12
 
< 0.1%
0.985 2
 
< 0.1%
0.984 2
 
< 0.1%

Key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.260936
Minimum0
Maximum11
Zeros13763
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:15.115035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5964355
Coefficient of variation (CV)0.68361134
Kurtosis-1.3067847
Mean5.260936
Median Absolute Deviation (MAD)3
Skewness0.019440125
Sum695259
Variance12.934348
MonotonicityNot monotonic
2025-06-23T12:37:15.225373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 17679
13.4%
0 13763
10.4%
7 13325
10.1%
2 11476
8.7%
11 11393
8.6%
9 10777
8.2%
8 10753
8.1%
6 10687
8.1%
5 10511
8.0%
4 9216
7.0%
Other values (2) 12575
9.5%
ValueCountFrequency (%)
0 13763
10.4%
1 17679
13.4%
2 11476
8.7%
3 3754
 
2.8%
4 9216
7.0%
5 10511
8.0%
6 10687
8.1%
7 13325
10.1%
8 10753
8.1%
9 10777
8.2%
ValueCountFrequency (%)
11 11393
8.6%
10 8821
6.7%
9 10777
8.2%
8 10753
8.1%
7 13325
10.1%
6 10687
8.1%
5 10511
8.0%
4 9216
7.0%
3 3754
 
2.8%
2 11476
8.7%

Loudness
Real number (ℝ)

High correlation 

Distinct5880
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.956753
Minimum-54.341
Maximum0.175
Zeros0
Zeros (%)0.0%
Negative132151
Negative (%)> 99.9%
Memory size2.0 MiB
2025-06-23T12:37:15.368824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-54.341
5-th percentile-9.809
Q1-7.031
median-5.629
Q3-4.485
95-th percentile-3.081
Maximum0.175
Range54.516
Interquartile range (IQR)2.546

Descriptive statistics

Standard deviation2.3131132
Coefficient of variation (CV)-0.3883178
Kurtosis30.923186
Mean-5.956753
Median Absolute Deviation (MAD)1.257
Skewness-2.9855079
Sum-787214.7
Variance5.3504927
MonotonicityNot monotonic
2025-06-23T12:37:15.540063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.251 169
 
0.1%
-3.838 149
 
0.1%
-5.042 146
 
0.1%
-4.88 145
 
0.1%
-4.501 137
 
0.1%
-5.569 132
 
0.1%
-5.611 127
 
0.1%
-5.692 126
 
0.1%
-6.684 124
 
0.1%
-6.986 122
 
0.1%
Other values (5870) 130778
99.0%
ValueCountFrequency (%)
-54.341 3
 
< 0.1%
-46.113 18
 
< 0.1%
-35.032 11
 
< 0.1%
-33.8 5
 
< 0.1%
-32.756 4
 
< 0.1%
-32.45 5
 
< 0.1%
-32.354 6
 
< 0.1%
-32.187 4
 
< 0.1%
-30.487 4
 
< 0.1%
-29.598 74
0.1%
ValueCountFrequency (%)
0.175 4
 
< 0.1%
-0.463 1
 
< 0.1%
-0.517 1
 
< 0.1%
-0.716 14
< 0.1%
-0.804 16
< 0.1%
-0.884 4
 
< 0.1%
-0.929 1
 
< 0.1%
-0.945 1
 
< 0.1%
-0.949 19
< 0.1%
-0.982 15
< 0.1%

Mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
1.0
87929 
0.0
44226 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters396465
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 87929
66.5%
0.0 44226
33.5%

Length

2025-06-23T12:37:15.700877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T12:37:15.781516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 87929
66.5%
0.0 44226
33.5%

Most occurring characters

ValueCountFrequency (%)
0 176381
44.5%
. 132155
33.3%
1 87929
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176381
44.5%
. 132155
33.3%
1 87929
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176381
44.5%
. 132155
33.3%
1 87929
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176381
44.5%
. 132155
33.3%
1 87929
22.2%

Speechiness
Real number (ℝ)

Distinct1209
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10105085
Minimum0.0225
Maximum0.741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:15.898530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0225
5-th percentile0.0277
Q10.0356
median0.0535
Q30.122
95-th percentile0.338
Maximum0.741
Range0.7185
Interquartile range (IQR)0.0864

Descriptive statistics

Standard deviation0.10186639
Coefficient of variation (CV)1.0080706
Kurtosis3.0966661
Mean0.10105085
Median Absolute Deviation (MAD)0.0227
Skewness1.8704181
Sum13354.375
Variance0.010376761
MonotonicityNot monotonic
2025-06-23T12:37:16.054704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337 548
 
0.4%
0.0295 501
 
0.4%
0.0334 497
 
0.4%
0.029 496
 
0.4%
0.0299 484
 
0.4%
0.0282 452
 
0.3%
0.0338 446
 
0.3%
0.0281 444
 
0.3%
0.0311 441
 
0.3%
0.0278 440
 
0.3%
Other values (1199) 127406
96.4%
ValueCountFrequency (%)
0.0225 20
 
< 0.1%
0.0227 20
 
< 0.1%
0.0228 35
 
< 0.1%
0.0229 20
 
< 0.1%
0.0231 67
0.1%
0.0232 135
0.1%
0.0233 27
 
< 0.1%
0.0234 25
 
< 0.1%
0.0235 21
 
< 0.1%
0.0236 30
 
< 0.1%
ValueCountFrequency (%)
0.741 3
 
< 0.1%
0.74 8
 
< 0.1%
0.699 3
 
< 0.1%
0.691 1
 
< 0.1%
0.684 1
 
< 0.1%
0.649 33
< 0.1%
0.643 8
 
< 0.1%
0.642 1
 
< 0.1%
0.632 1
 
< 0.1%
0.628 1
 
< 0.1%

Acousticness
Real number (ℝ)

Distinct2733
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16926608
Minimum1.45 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:16.223249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.45 × 10-6
5-th percentile0.00143
Q10.0209
median0.0841
Q30.246
95-th percentile0.624
Maximum0.996
Range0.99599855
Interquartile range (IQR)0.2251

Descriptive statistics

Standard deviation0.20404334
Coefficient of variation (CV)1.2054591
Kurtosis2.1299381
Mean0.16926608
Median Absolute Deviation (MAD)0.07574
Skewness1.6259908
Sum22369.358
Variance0.041633683
MonotonicityNot monotonic
2025-06-23T12:37:16.390705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.103 435
 
0.3%
0.111 412
 
0.3%
0.119 402
 
0.3%
0.107 400
 
0.3%
0.191 391
 
0.3%
0.141 389
 
0.3%
0.114 381
 
0.3%
0.259 360
 
0.3%
0.199 334
 
0.3%
0.189 328
 
0.2%
Other values (2723) 128323
97.1%
ValueCountFrequency (%)
1.45 × 10-63
 
< 0.1%
2.73 × 10-63
 
< 0.1%
3.28 × 10-64
 
< 0.1%
3.29 × 10-61
 
< 0.1%
7.81 × 10-612
< 0.1%
9.4 × 10-620
< 0.1%
1.21 × 10-520
< 0.1%
1.4 × 10-520
< 0.1%
1.41 × 10-51
 
< 0.1%
1.52 × 10-52
 
< 0.1%
ValueCountFrequency (%)
0.996 5
 
< 0.1%
0.994 13
< 0.1%
0.987 2
 
< 0.1%
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 32
< 0.1%
0.975 4
 
< 0.1%
0.974 1
 
< 0.1%
0.972 2
 
< 0.1%
0.971 1
 
< 0.1%

Instrumentalness
Real number (ℝ)

Zeros 

Distinct2419
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0074605168
Minimum0
Maximum0.995
Zeros79224
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:16.544815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.4 × 10-5
95-th percentile0.00488
Maximum0.995
Range0.995
Interquartile range (IQR)1.4 × 10-5

Descriptive statistics

Standard deviation0.062435242
Coefficient of variation (CV)8.3687556
Kurtosis138.35637
Mean0.0074605168
Median Absolute Deviation (MAD)0
Skewness11.326584
Sum985.9446
Variance0.0038981594
MonotonicityNot monotonic
2025-06-23T12:37:16.766145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79224
59.9%
1.16 × 10-6165
 
0.1%
0.000198 162
 
0.1%
2.97 × 10-6156
 
0.1%
1.11 × 10-6149
 
0.1%
1.35 × 10-6146
 
0.1%
1.75 × 10-6146
 
0.1%
2.06 × 10-6138
 
0.1%
1.22 × 10-5135
 
0.1%
1.29 × 10-5131
 
0.1%
Other values (2409) 51603
39.0%
ValueCountFrequency (%)
0 79224
59.9%
1 × 10-670
 
0.1%
1.01 × 10-6111
 
0.1%
1.02 × 10-650
 
< 0.1%
1.03 × 10-6127
 
0.1%
1.04 × 10-691
 
0.1%
1.05 × 10-665
 
< 0.1%
1.06 × 10-662
 
< 0.1%
1.07 × 10-655
 
< 0.1%
1.08 × 10-658
 
< 0.1%
ValueCountFrequency (%)
0.995 16
< 0.1%
0.985 10
< 0.1%
0.977 5
 
< 0.1%
0.956 4
 
< 0.1%
0.953 1
 
< 0.1%
0.944 4
 
< 0.1%
0.937 2
 
< 0.1%
0.927 2
 
< 0.1%
0.924 6
 
< 0.1%
0.918 3
 
< 0.1%

Liveness
Real number (ℝ)

Distinct1292
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17699721
Minimum0.0165
Maximum0.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:16.919513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0165
5-th percentile0.057
Q10.0933
median0.123
Q30.224
95-th percentile0.429
Maximum0.979
Range0.9625
Interquartile range (IQR)0.1307

Descriptive statistics

Standard deviation0.13374865
Coefficient of variation (CV)0.75565403
Kurtosis5.2130852
Mean0.17699721
Median Absolute Deviation (MAD)0.043
Skewness2.0592995
Sum23391.066
Variance0.017888702
MonotonicityNot monotonic
2025-06-23T12:37:17.091782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107 1610
 
1.2%
0.106 1538
 
1.2%
0.114 1474
 
1.1%
0.11 1328
 
1.0%
0.111 1317
 
1.0%
0.105 1296
 
1.0%
0.101 1284
 
1.0%
0.109 1227
 
0.9%
0.108 1220
 
0.9%
0.112 1198
 
0.9%
Other values (1282) 118663
89.8%
ValueCountFrequency (%)
0.0165 3
 
< 0.1%
0.0193 8
 
< 0.1%
0.02 1
 
< 0.1%
0.0207 3
 
< 0.1%
0.021 24
< 0.1%
0.0214 4
 
< 0.1%
0.0215 23
< 0.1%
0.0216 9
 
< 0.1%
0.0217 20
< 0.1%
0.0219 1
 
< 0.1%
ValueCountFrequency (%)
0.979 19
< 0.1%
0.976 2
 
< 0.1%
0.971 12
< 0.1%
0.963 3
 
< 0.1%
0.962 12
< 0.1%
0.959 10
< 0.1%
0.953 3
 
< 0.1%
0.952 1
 
< 0.1%
0.948 12
< 0.1%
0.947 1
 
< 0.1%

Valence
Real number (ℝ)

Distinct1081
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52148975
Minimum1 × 10-5
Maximum0.976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:17.242541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.162
Q10.349
median0.522
Q30.695
95-th percentile0.888
Maximum0.976
Range0.97599
Interquartile range (IQR)0.346

Descriptive statistics

Standard deviation0.22153168
Coefficient of variation (CV)0.42480543
Kurtosis-0.86753373
Mean0.52148975
Median Absolute Deviation (MAD)0.173
Skewness0.012456692
Sum68917.478
Variance0.049076284
MonotonicityNot monotonic
2025-06-23T12:37:17.414474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.376 410
 
0.3%
0.464 373
 
0.3%
0.437 367
 
0.3%
0.472 364
 
0.3%
0.331 354
 
0.3%
0.527 342
 
0.3%
0.7 338
 
0.3%
0.332 337
 
0.3%
0.531 337
 
0.3%
0.74 336
 
0.3%
Other values (1071) 128597
97.3%
ValueCountFrequency (%)
1 × 10-511
< 0.1%
0.032 19
< 0.1%
0.0337 7
 
< 0.1%
0.0344 4
 
< 0.1%
0.0349 3
 
< 0.1%
0.0351 2
 
< 0.1%
0.0352 16
< 0.1%
0.0361 1
 
< 0.1%
0.0363 1
 
< 0.1%
0.0365 6
 
< 0.1%
ValueCountFrequency (%)
0.976 39
 
< 0.1%
0.974 1
 
< 0.1%
0.973 13
 
< 0.1%
0.972 44
 
< 0.1%
0.971 31
 
< 0.1%
0.97 13
 
< 0.1%
0.969 110
0.1%
0.968 25
 
< 0.1%
0.967 96
0.1%
0.966 29
 
< 0.1%

Time Signature
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
4.0
125161 
3.0
 
4797
5.0
 
1772
1.0
 
425

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters396465
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 125161
94.7%
3.0 4797
 
3.6%
5.0 1772
 
1.3%
1.0 425
 
0.3%

Length

2025-06-23T12:37:17.559574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T12:37:17.641844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 125161
94.7%
3.0 4797
 
3.6%
5.0 1772
 
1.3%
1.0 425
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 132155
33.3%
0 132155
33.3%
4 125161
31.6%
3 4797
 
1.2%
5 1772
 
0.4%
1 425
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 132155
33.3%
0 132155
33.3%
4 125161
31.6%
3 4797
 
1.2%
5 1772
 
0.4%
1 425
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 132155
33.3%
0 132155
33.3%
4 125161
31.6%
3 4797
 
1.2%
5 1772
 
0.4%
1 425
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 396465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 132155
33.3%
0 132155
33.3%
4 125161
31.6%
3 4797
 
1.2%
5 1772
 
0.4%
1 425
 
0.1%

Label
Text

Distinct1350
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2025-06-23T12:37:17.867699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length117
Median length71
Mean length18.203541
Min length1

Characters and Unicode

Total characters2405689
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)0.1%

Sample

1st rowColumbia/Legacy
2nd rowMotown
3rd rowColumbia
4th rowArista
5th rowAtlantic Records/ATG
ValueCountFrequency (%)
records 53646
 
15.9%
nashville 13174
 
3.9%
music 9508
 
2.8%
9466
 
2.8%
label 7880
 
2.3%
rca 5779
 
1.7%
columbia 5673
 
1.7%
atlantic 5482
 
1.6%
llc 5453
 
1.6%
capitol 5184
 
1.5%
Other values (1691) 215135
64.0%
2025-06-23T12:37:18.274859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 206764
 
8.6%
204225
 
8.5%
o 154333
 
6.4%
r 148797
 
6.2%
s 134447
 
5.6%
c 127420
 
5.3%
a 125925
 
5.2%
i 124237
 
5.2%
l 103760
 
4.3%
n 99272
 
4.1%
Other values (78) 976509
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2405689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 206764
 
8.6%
204225
 
8.5%
o 154333
 
6.4%
r 148797
 
6.2%
s 134447
 
5.6%
c 127420
 
5.3%
a 125925
 
5.2%
i 124237
 
5.2%
l 103760
 
4.3%
n 99272
 
4.1%
Other values (78) 976509
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2405689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 206764
 
8.6%
204225
 
8.5%
o 154333
 
6.4%
r 148797
 
6.2%
s 134447
 
5.6%
c 127420
 
5.3%
a 125925
 
5.2%
i 124237
 
5.2%
l 103760
 
4.3%
n 99272
 
4.1%
Other values (78) 976509
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2405689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 206764
 
8.6%
204225
 
8.5%
o 154333
 
6.4%
r 148797
 
6.2%
s 134447
 
5.6%
c 127420
 
5.3%
a 125925
 
5.2%
i 124237
 
5.2%
l 103760
 
4.3%
n 99272
 
4.1%
Other values (78) 976509
40.6%

Track Duration (min)
Real number (ℝ)

High correlation 

Distinct8765
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7154962
Minimum0.57333333
Maximum12.3335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-06-23T12:37:18.383901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.57333333
5-th percentile2.6465583
Q13.2682167
median3.6717667
Q34.0904333
95-th percentile4.9144333
Maximum12.3335
Range11.760167
Interquartile range (IQR)0.82221667

Descriptive statistics

Standard deviation0.72592782
Coefficient of variation (CV)0.19537843
Kurtosis5.3881906
Mean3.7154962
Median Absolute Deviation (MAD)0.41111667
Skewness0.97075195
Sum491021.41
Variance0.5269712
MonotonicityNot monotonic
2025-06-23T12:37:18.531445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.86555 153
 
0.1%
3.334 110
 
0.1%
3.651333333 107
 
0.1%
4.266666667 106
 
0.1%
3.817333333 106
 
0.1%
3.193333333 103
 
0.1%
3.87555 101
 
0.1%
3.558216667 100
 
0.1%
3.1011 100
 
0.1%
3.822666667 98
 
0.1%
Other values (8755) 131071
99.2%
ValueCountFrequency (%)
0.5733333333 3
 
< 0.1%
0.6168833333 1
 
< 0.1%
0.6622 13
< 0.1%
0.69145 1
 
< 0.1%
0.7892333333 1
 
< 0.1%
0.8 1
 
< 0.1%
0.8215333333 1
 
< 0.1%
0.8699166667 1
 
< 0.1%
0.8907 1
 
< 0.1%
1.146666667 17
< 0.1%
ValueCountFrequency (%)
12.3335 1
 
< 0.1%
11.47421667 22
< 0.1%
10.33501667 1
 
< 0.1%
10.2171 1
 
< 0.1%
9.802316667 1
 
< 0.1%
9.7894 2
 
< 0.1%
9.128883333 13
< 0.1%
8.9756 2
 
< 0.1%
8.9327 13
< 0.1%
8.83755 1
 
< 0.1%

Interactions

2025-06-23T12:37:02.447778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:27.461708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.625130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.779561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.798876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.065393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.507475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.729766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.906334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.991659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.108595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.139874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.536532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.916420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.208768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.120883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.588584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:27.594552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.755271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.898463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.922836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.200907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.659911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.846011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.020625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.112216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.255876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.309937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.668530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.056277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.350781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.277545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.732552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:27.717586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.873164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.016209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.040637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.333348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.827776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.970531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.159681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.234185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.406164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.453121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.826391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.194275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.484972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.422373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.868618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:27.836928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.991714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.134116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.159726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.458683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.974995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.089854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.285961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.355025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.567913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.587456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.992199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.332191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.616527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.578518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.002021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:27.960549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.112271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.282892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.286620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.593278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.130896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.220623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.449898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.503122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.733756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.720720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.125174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.507472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.758744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.729615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.166551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.089420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.249957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.429540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.419896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.721722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.283474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.367685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.586258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.634029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:47.903952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:50.867695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.268649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.683249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.911767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:00.887827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.299165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.273205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.373227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.555254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.543410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.869105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.425056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.713012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.719939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.766088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:48.070882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.004535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.409648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.817698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.067539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.019829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.435992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.412453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.494747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.673128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.675509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:36.993438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.579857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.828494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.846189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:45.885185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:48.225182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.180782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.554176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:55.949783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.227596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.158557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.569982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.552189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.757503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.792205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.797785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.155804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.718900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:41.946296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:43.979581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.002871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:48.412533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.347822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.686730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.084150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.390713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.289448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.731966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.769779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:30.891704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:32.921988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:34.920743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.314278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.834210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.069867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.102540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.124541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:48.593571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.492107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.830996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.211710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.552007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.458667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:03.872541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:28.894080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.014064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.040788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.064474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.475696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:39.953201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.186271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.234403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.255433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:48.759770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.650936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:53.983100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.359854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.720811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.604619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:04.018567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.022435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.149869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.168568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.370864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.645302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.080787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.309825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.363245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.393201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:49.295383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.817519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.132525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.510908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:58.934407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.771006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:04.161441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.141897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.266624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.294419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.538797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.820863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.200571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.438571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.495962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.519978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:49.480946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:51.970566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.297683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.647652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:59.458145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:01.897835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:04.310417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.260837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.396358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.416405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.669599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:37.964257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.326053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.549667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.618879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.645228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:49.643347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.102686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.456287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.776124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:59.606420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.029549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:04.485026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.381475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.525027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.536663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.806103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.137905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.446229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.669943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.739647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.776461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:49.817581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.238667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.599881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:56.924303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:59.768850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.179661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:04.646194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:29.503332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:31.643000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:33.664219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:35.928797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:38.318652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:40.604647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:42.781699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:44.867889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:46.952546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:49.978538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:52.401484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:54.758499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:57.060594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:36:59.929917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T12:37:02.313783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-23T12:37:18.654028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AcousticnessDanceabilityEnergyExplicitInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime SignatureTrack Duration (min)Track Duration (ms)Valencelast_weeks_rankweeks_on_chartweeks_rank
Acousticness1.000-0.032-0.4290.086-0.0900.003-0.058-0.2970.0660.016-0.082-0.0990.091-0.019-0.019-0.0840.0170.0090.008
Danceability-0.0321.000-0.1270.374-0.002-0.000-0.102-0.0660.1830.0780.350-0.1700.124-0.110-0.1100.333-0.1080.017-0.111
Energy-0.429-0.1271.0000.1780.0120.0320.1200.6810.054-0.0570.0610.1110.095-0.032-0.0320.3950.020-0.0110.035
Explicit0.0860.3740.1781.0000.0390.1000.0790.0960.1440.1330.4780.0800.0580.1690.1690.0830.0520.0680.030
Instrumentalness-0.090-0.0020.0120.0391.0000.004-0.043-0.1130.0370.071-0.1280.0180.0440.0610.061-0.0630.013-0.0020.013
Key0.003-0.0000.0320.1000.0041.0000.0110.0260.273-0.0040.0310.0150.054-0.004-0.0040.0250.002-0.0010.003
Liveness-0.058-0.1020.1200.079-0.0430.0111.0000.0570.036-0.0260.0420.0320.032-0.009-0.009-0.0320.029-0.0360.027
Loudness-0.297-0.0660.6810.096-0.1130.0260.0571.0000.0360.020-0.0070.0710.067-0.056-0.0560.263-0.0200.011-0.008
Mode0.0660.1830.0540.1440.0370.2730.0360.0361.0000.0940.1540.0840.0400.0460.0460.0560.0790.0310.075
Popularity0.0160.078-0.0570.1330.071-0.004-0.0260.0200.0941.000-0.0020.0010.053-0.128-0.128-0.054-0.3640.277-0.382
Speechiness-0.0820.3500.0610.478-0.1280.0310.042-0.0070.154-0.0021.0000.0270.097-0.038-0.0380.144-0.035-0.066-0.043
Tempo-0.099-0.1700.1110.0800.0180.0150.0320.0710.0840.0010.0271.0000.084-0.041-0.041-0.0180.029-0.0140.029
Time Signature0.0910.1240.0950.0580.0440.0540.0320.0670.0400.0530.0970.0841.0000.0180.0180.0880.0230.0260.021
Track Duration (min)-0.019-0.110-0.0320.1690.061-0.004-0.009-0.0560.046-0.128-0.038-0.0410.0181.0001.000-0.139-0.0550.022-0.046
Track Duration (ms)-0.019-0.110-0.0320.1690.061-0.004-0.009-0.0560.046-0.128-0.038-0.0410.0181.0001.000-0.139-0.0550.022-0.046
Valence-0.0840.3330.3950.083-0.0630.025-0.0320.2630.056-0.0540.144-0.0180.088-0.139-0.1391.000-0.0600.033-0.056
last_weeks_rank0.017-0.1080.0200.0520.0130.0020.029-0.0200.079-0.364-0.0350.0290.023-0.055-0.055-0.0601.000-0.5630.868
weeks_on_chart0.0090.017-0.0110.068-0.002-0.001-0.0360.0110.0310.277-0.066-0.0140.0260.0220.0220.033-0.5631.000-0.420
weeks_rank0.008-0.1110.0350.0300.0130.0030.027-0.0080.075-0.382-0.0430.0290.021-0.046-0.046-0.0560.868-0.4201.000

Missing values

2025-06-23T12:37:04.980793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-23T12:37:05.562182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datesong_nameartistweeks_ranklast_weeks_rankweeks_on_chartTrack NameTempouriArtist Name(s)Album NameAlbum Release DateTrack Duration (ms)ExplicitPopularityDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTime SignatureLabelTrack Duration (min)
02000-01-02SmoothSantana Featuring Rob Thomas11.024Smooth (feat. Rob Thomas)115.996spotify:track:0n2SEXB2qoRQg171q7XqeWSantana, Rob ThomasSupernatural (Remastered)1999-06-15294986.0False73.00.6090.9239.0-3.9081.00.03380.1600000.0000050.29500.96104.0Columbia/Legacy4.916433
12000-01-02Back At OneBrian McKnight22.020Back At One129.747spotify:track:6mwA6YiKDjAUG8kWvRRUPhBrian McKnightBack At One1999-09-21263666.0False72.00.6600.34711.0-9.1141.00.03000.4520000.0000000.13100.25604.0Motown4.394433
22000-01-02I Wanna Love You ForeverJessica Simpson33.013I Wanna Love You Forever104.042spotify:track:5gZEhPrN1VLqTG1nIAXeNKJessica SimpsonSweet Kisses1999-11-16263800.0False55.00.5900.6664.0-4.0470.00.02440.1560000.0000000.13600.07434.0Columbia4.396667
32000-01-02My Love Is Your LoveWhitney Houston44.019My Love Is Your Love82.511spotify:track:1ckU1EhAO0Nr73QYw24SWJWhitney HoustonMy Love Is Your Love1998-11-17261573.0False69.00.7700.4757.0-9.5121.00.15900.0561000.0000050.06070.47304.0Arista4.359550
42000-01-02Hot BoyzMissy "Misdemeanor" Elliott Featuring NAS, EVE & Q-Tip57.07Hot Boyz81.125spotify:track:7mYvtEeBdMqRSyj1Qpv6myMissy ElliottDa Real World1999215466.0True52.00.7270.4451.0-11.2411.00.29100.3390000.0000000.18000.52704.0Atlantic Records/ATG3.591100
52000-01-02I Knew I Loved YouSavage Garden65.012I Knew I Loved You85.031spotify:track:6nozDLxeL0TE4MS9GqYU1vSavage GardenAffirmation1999-11-09250360.0False72.00.6100.4979.0-9.8801.00.02790.3200000.0000610.08840.73904.0Columbia4.172667
62000-01-02Auld Lang SyneKenny G754.03Auld Lang Syne62.330spotify:track:7h4FywzqhgZrrvpiLDJ2vwKenny GThe Classic Christmas Album2012-09-21293346.0False27.00.1600.2960.0-11.8601.00.03480.1110000.8470000.10100.03884.0Arista/Legacy4.889100
72000-01-02I Need To KnowMarc Anthony86.018I Need to Know115.061spotify:track:7ffwRz8lZyDOE4Vj58Lo72Marc AnthonyMarc Anthony1999-09-28227706.0False55.00.8130.9493.0-2.5630.00.03360.4860000.0000100.11200.79204.0Columbia3.795100
82000-01-02Bring It All To MeBlaque99.012Bring It All to Me (feat. *NSYNC)88.957spotify:track:1cjBan0t4eBk2Y5j17hdyfBlaque, *NSYNCBlaque1999-05-28218186.0False59.00.6950.5790.0-5.0661.00.03250.0004790.0000000.05560.72604.0Sony Music Entertainment3.636433
92000-01-02U Know What's UpDonell Jones108.016U Know What's Up (feat. Lisa "Left Eye" Lopes)103.032spotify:track:5PMKzsUsTpZZGsCcJBuhP2Donell Jones, Lisa "Left Eye" LopesWhere I Wanna Be1999-05-29243733.0True67.00.8540.5438.0-6.1660.00.08440.0402000.0000570.04190.86804.0Arista/LaFace Records4.062217
datesong_nameartistweeks_ranklast_weeks_rankweeks_on_chartTrack NameTempouriArtist Name(s)Album NameAlbum Release DateTrack Duration (ms)ExplicitPopularityDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTime SignatureLabelTrack Duration (min)
1323902025-05-11My WorldChuckyy9179.02My World157.922spotify:track:5PCn4ysnzhILLhQY0u4AnsChuckyyMy World2025-04-24157979.0True76.00.6050.6731.0-9.7180.00.12200.0511000.0016100.38700.5964.0Santa Anna2.632983
1323912025-05-11GnarlyKATSEYE92101.01Gnarly135.021spotify:track:1j15Ar0qGDzIR0v3CQv3JLKATSEYEGnarly2025-04-30137316.0True88.00.7750.8280.0-5.1910.00.37300.1530000.0000000.13000.6634.0HYBE/Geffen2.288600
1323922025-05-11MorenaNeton Vega & Peso Pluma9390.06Morena118.007spotify:track:4oB8Xd7gMlUEtWoD8bmCXWNeton Vega, Peso PlumaMi Vida Mi Muerte2025-02-14193728.0False93.00.6970.7764.0-7.4090.00.03080.3790000.0014800.34100.1893.0Josa Records3.228800
1323932025-05-11Holy SmokesBailey Zimmerman9473.016Holy Smokes95.117spotify:track:1kMWJ16W3Yk3hyNmaM7jfQBailey ZimmermanHoly Smokes2024-02-23195157.0False78.00.4620.5697.0-7.9081.00.03210.5090000.0000000.12000.4324.0Warner Music Nashville/Elektra3.252617
1323942025-05-11Me PrometiIvan Cornejo95101.01Me Prometí81.896spotify:track:5uXL0CN1mQxHJSJRHN44OdIvan CornejoMe Prometí2025-05-01171788.0False81.00.5390.3597.0-10.0491.00.02770.7860000.0000010.33000.1704.0Interscope Records2.863133
1323952025-05-11BMFSZA9682.020BMF128.023spotify:track:3U3hFkMr0Q90pD24EkE3PrSZASOS Deluxe: LANA2024-12-20180746.0True88.00.7920.55010.0-8.5280.00.03070.5060000.0166000.08700.9724.0Top Dawg Entertainment/RCA Records3.012433
1323962025-05-11HauntedKane Brown With Jelly Roll9795.08Liar114.011spotify:track:0llPOBVoJYDAtdmVlNE41AJelly RollBeautifully Broken2024-10-10204933.0False82.00.6450.7671.0-5.2021.00.02990.0405000.0000000.09200.6134.0BBR Music Group/Jelly Roll3.415550
1323972025-05-11Steve's Lava ChickenJack Black9888.03Steve's Lava Chicken157.629spotify:track:2n5sAzeWh5LqnV9cGBjgGrJack BlackA Minecraft Movie (Original Motion Picture Soundtrack)2025-03-2834400.0False84.00.7220.7315.0-6.8800.00.11300.0003060.0000830.07330.3634.0WaterTower Music0.573333
1323982025-05-11The DaysCHRYSTAL99101.06The Days124.992spotify:track:637HB7HZWWsBbhoTIzDppXChrystalThe Days (NOTION Remix)2024-11-08169412.0False49.00.9170.6229.0-8.4350.00.05610.0059600.0000070.06580.4914.0Polydor Records2.823533
1323992025-05-11Die TryingPARTYNEXTDOOR, Drake & Yebba10092.012DIE TRYING110.497spotify:track:0NUqi0ps17YpLUC3kgsZq0PARTYNEXTDOOR, Drake, Yebba$ome $exy $ongs 4 U2025-02-14195431.0True88.00.7630.53111.0-6.1541.00.03280.1460000.0000000.17500.6654.0OVO Sound3.257183